Award-winning LMS provider for enterprises and mid-size organizations came to Armakuni for genAI assessment and content pipeline: LLM-powered exam ingestion, zero-ETL analytics, Amazon Bedrock model optimization, DevOps modernization.
The challenge
Armakuni entered through a structured assessment process that mapped Award-winning LMS provider for enterprises and mid-size organizations's GenAI readiness from data to deployment. The initial Rapid Assessment SOW funded entirely by AWS at $40,000 grew directly into a flexible engineering PoD engagement. Armakuni now provides a dedicated DevOps resource plus access to application engineers, data engineers, and solution architects across a monthly retainer.
Scale: Enterprise and mid-market LMS customers globally; 8-9 exam question types across diverse content formats (PDF, Word, and more).
What we built
Armakuni delivered genAI assessment and content pipeline: LLM-powered exam ingestion, zero-ETL analytics, Amazon Bedrock model optimization, DevOps modernization. The engagement ran as two SOWs: (1) Rapid Assessment - Tier 2 GenAI Use Case Discovery Assessment; (2) Flexible Engineering PoD - DevOps resource retainer. AWS funded the work at $40,000.
The outcomes
6 measurable outcomes shipped across the engagement. The ones that moved the business the most:
A $40,000 assessment delivered at zero cost to the business. It provided a comprehensive GenAI readiness evaluation, model benchmarking, and a fully scoped implementation plan without committing a dollar of the company's own budget.
An LLM-powered pipeline to cut exam content processing time across all 8 to 9 question types. What previously required manual SME intervention for every upload is being replaced by automated ingestion that normalizes format and structure before content reaches the LMS.
Amazon Bedrock selected as the foundation model layer with flexible model switching. The company can balance cost and performance across content generation tasks without rearchitecting the pipeline when model requirements change.
Zero-ETL architecture enables real-time analytics without migrating off MySQL. Reporting and decision-making capabilities expand immediately, without requiring a database migration or the disruption that comes with it.
250 hours of engineering capacity per month deployed right after the assessment closed. The structure lets the company shift its skill mix across data science, ML, and application engineering as the platform moves from DevOps stabilization into GenAI implementation.
The AWS account manager cited Armakuni's ability to hold C-suite trust through a six-month hold. That partnership depth keeps engagements alive through disruption and positions Armakuni as the team the company calls when the GenAI build begins in earnest.
The Armakuni team demonstrated an impressive ability to earn customer trust and deliver against lofty expectations with the customer C-Suite. Ruben and team maintained consistent communication with the customer, even after initial projects were put on hold for half a year.
David Nacson, AWS Account Manager
Built on AWS
Delivered under our AWS AI Services, Data & Analytics Consulting competencies.
What's next
Implementation of LLM-powered content pipeline on Amazon Bedrock; zero-ETL architecture deployment; potential expansion of PoD to include data science and ML engineering resources.